Meta-Analysis on the Effect of Interventions Used in Cattle Processing Plants to Reduce Escherichia coli Contamination in Beef

• Main Author: Zhilyaev, Samson
• Other Authors: Civil and Environmental Engineering
• Format: Others
• Published: Virginia Tech 2017
• Subjects: QMRA; meta-analysis; Escherichia; coli; O157; meta-regression; plant; intervention; beef; cattle; STEC
• Online Access: http://hdl.handle.net/10919/81184

A Quantitative Microbial Risk Assessment (QMRA) has been undertaken to utilize research on Shiga-toxin Escherichia coli (STEC) contamination in beef for the benefit of public health. The QMRA operates as a 2nd order Monte Carlo simulation to create stochastic mathematical models that incorporate all of the key components of STEC contamination from farm to fork. The resulting model is able to identify knowledge gaps, public health risks, and simulate theoretical changes in the beef system. However, high variability in processing plant intervention literature has prompted a meta-analysis to determine informed estimates of intervention effectiveness for QMRA parameterization. Meta-analysis derived least-squares means bacterial log reductions for acetic acid, lactic acid, steam vacuum, and water wash interventions on carcass surfaces (n=249) were 1.44 [95% CI: 0.73 – 2.15], 2.07 [1.48 – 2.65], 3.09 [2.46 – 3.73], and 1.90 [1.33 – 2.47] log CFU/cm2, respectively. Least-squares means log reductions for acetic acid, lactic acid, sodium hydroxide, and water wash on hide surfaces (n=47) were 2.21 [1.36 – 3.05], 3.02 [2.16 – 3.88], 3.66 [2.60 – 4.72], and 0.08 [-0.94 – 1.11] log CFU/cm2, respectively. Meta-regressions showed that temperature, duration of application, microbial starting concentration, extra water washes, inoculation type, organism type, sample method, surface type, and antimicrobial concentrations were all significant predictors of intervention effectiveness. Finally, after observing authors use substituted values for samples found below a detection limit in primary plant intervention literature, simulations were run to assess the impact of substitution on a random-effects meta-analysis. Simulation results show that substitution practices artificially decrease effectiveness estimates and increase heterogeneity. === Master of Science